Introduction to Operations Research, Volume 1CD-ROM contains: Student version of MPL Modeling System and its solver CPLEX -- MPL tutorial -- Examples from the text modeled in MPL -- Examples from the text modeled in LINGO/LINDO -- Tutorial software -- Excel add-ins: TreePlan, SensIt, RiskSim, and Premium Solver -- Excel spreadsheet formulations and templates. |
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Page 11
... function is called the objective function . Any restrictions on the values that can be assigned to these decision variables are also expressed mathematically , typically by means of inequalities or equations ( for example , x1 + ...
... function is called the objective function . Any restrictions on the values that can be assigned to these decision variables are also expressed mathematically , typically by means of inequalities or equations ( for example , x1 + ...
Page 148
... objective function is essentially equivalent to the phase 1 objective function as long as x4 and / or x6 is greater than zero . Then , when both x4 = 0 and = 0 , the objective function for the Big M method becomes completely ...
... objective function is essentially equivalent to the phase 1 objective function as long as x4 and / or x6 is greater than zero . Then , when both x4 = 0 and = 0 , the objective function for the Big M method becomes completely ...
Page 273
... objective function coefficient is the only one being changed . How- ever , when simultaneous changes are made in the coefficients of the objective function , a 100 percent rule is available for checking whether the original solution ...
... objective function coefficient is the only one being changed . How- ever , when simultaneous changes are made in the coefficients of the objective function , a 100 percent rule is available for checking whether the original solution ...
Other editions - View all
Introduction to Operations Research Frederick S. Hillier,Gerald J. Lieberman No preview available - 2001 |
Common terms and phrases
activity algebraic algorithm allowable range artificial variables b₂ basic solution c₁ c₂ changes coefficients column Consider the following corresponding cost Courseware CPF solution CPLEX decision variables dual problem dynamic programming entering basic variable estimates example feasible region feasible solutions final simplex tableau final tableau flow following problem formulation functional constraints Gaussian elimination given graphical identify increase initial BF solution integer interior-point iteration leaving basic variable linear programming model linear programming problem LINGO LP relaxation lution Maximize subject Maximize Z maximum flow problem Minimize needed node nonbasic variables nonnegativity constraints objective function obtained optimal solution optimality test parameters path plant presented in Sec primal problem Prob procedure range to stay right-hand sides sensitivity analysis shadow prices simplex method slack variables solve the model Solver spreadsheet step subproblem surplus variables Table tion values weeks Wyndor Glass x₁ zero